论文标题
使用机器学习来检测2D图像中反射对称性的旋转对称性
Using Machine Learning to Detect Rotational Symmetries from Reflectional Symmetries in 2D Images
论文作者
论文摘要
自动对称检测在2021年仍然是一项艰巨的任务。但是，它在计算机视觉中有应用，并且在理解艺术中也起着重要的作用。本文通过比较不同的最新自动对称检测算法来帮助后者。对于针对反射对称性的这种算法之一，我们提出了后处理的改进，以在图像中找到局部对称性，改善检测到的对称性的选择并识别另一种对称类型（旋转）。为了检测旋转对称性，我们贡献了一个机器学习模型，该模型根据提供的反射对称轴对检测旋转对称性。我们演示和分析扩展算法的性能，以检测局部对称性和机器学习模型以对旋转对称性进行分类。
Automated symmetry detection is still a difficult task in 2021. However, it has applications in computer vision, and it also plays an important part in understanding art. This paper focuses on aiding the latter by comparing different state-of-the-art automated symmetry detection algorithms. For one of such algorithms aimed at reflectional symmetries, we propose post-processing improvements to find localised symmetries in images, improve the selection of detected symmetries and identify another symmetry type (rotational). In order to detect rotational symmetries, we contribute a machine learning model which detects rotational symmetries based on provided reflection symmetry axis pairs. We demonstrate and analyze the performance of the extended algorithm to detect localised symmetries and the machine learning model to classify rotational symmetries.
